King County House Sales

King County House Sales Dataset

The King County House Sales dataset contains the house sales in King County (USA), between May 2014 and May 2015. The original dataset can be found on Kaggle.

All the files of this project are saved in a GitHub repository.

The dataset consists in:

• a Train Set with 17,277 observations with 19 house features, the ID, and the price,
  
• a Test Set with 4,320 observations with 19 house features and the ID.

This map shows where the properties listed in the dataset are located:

This project aims to predict the house price based on its features.

Packages

This analysis requires these R packages:

• Data Cleaning: tidyr

• Plotting: ggplot2, corrplot, GGally, gridExtra

• Statistics: caret

These packages are installed and loaded if necessary by the main script.

Data Preparation

The dataset is pretty clean, with no missing value in both Train and Test sets. A quick look at the id values also shows that only a few houses have been sold more than once on the period, so it doesn’t seem relevant the consider the id and the date for this analysis. We will thus focus on the house features only to predict its price.

## [1] "0 columns of the Train Set have NAs."
## [1] "1 columns of the Test Set have NAs."
## [1] "103 houses sold more than once on the period, for a total of 17173 houses in the Train Set (0.006%)."
## [1] "8 houses in the Test Set sold more than once on the period, for a total of 4312 houses in the Test Set (0.002%)."

Some of the features can be converted into factors, as they are not continuous and help categorizing the house:

• waterfront is a boolean, indicating if the house has a view to a waterfront.
  
• view indicated the number of times the house has been viewed. As it only has 4 values, this feature can be factorized.
  
• condition is a score from 1 to 5, indicating the state of the house.
  
• grade is another score, with 11 levels.
  
• bedrooms is typically used to categorize houses.
  
• bathrooms could also be used to categorize the houses, although it is not clear why the data shows decimal numbers.
  
• zipcode is a cluster-like category of the house location.

Exploratory Data Analysis